📝 SummarXiv

Abstract

Large language model-based chatbots have enhanced engagement in financial negotiations, but their overreliance on passive empathy introduces critical risks in credit collection. While empathy-driven approaches preserve client satisfaction in benign cases, they fail catastrophically against dishonest debtors--individuals who exploit conciliatory tactics to manipulate terms or evade repayment. Blindly prioritizing "customer experience" in such scenarios leads to creditor vulnerabilities: revenue leakage, moral hazard, and systemic exploitation. To address this, we propose EQ-Knight, an LLM agent that dynamically optimizes emotional strategy to defend creditor interests. Unlike naive empathy-centric bots, EQ-Knight integrates emotion memory and game-theoretic reasoning, powered by a Hidden Markov Model (HMM) to track and predict debtor emotional states. By analyzing both real-time and historical emotional cues, EQ-Knight strategically counters negative emotions (e.g., aggression, feigned distress) while preserving productive debtor relationships. Experiments demonstrate EQ-Knight's superiority over conventional LLM negotiators: it achieves a 32\% reduction in concession losses without compromising recovery rates, particularly in adversarial cases where debtors weaponize negative emotions (e.g., intimidation, guilt-tripping) to coerce concessions. For credit agencies, EQ-Knight transforms LLMs from high-risk "people-pleasers" into strategic emotion-defenders--balancing emotional intelligence with tactical rigor to enforce accountability and deter exploitation.